A naukri.com initiative
Arxiv
2d
97
Image Credit: Arxiv
TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval
- TRACE is a new multimodal retriever proposed for grounding time-series data in textual context.
- Dynamic data in areas like weather, healthcare, and energy require effective interpretation and retrieval.
- TRACE addresses the lack of semantic grounding in time-series retrieval methods.
- It aligns time-series embeddings with textual context and supports various cross-modal retrieval modes.
- The retriever facilitates linking linguistic descriptions with complex temporal patterns.
- TRACE enriches downstream models with context to improve predictive accuracy and interpretability.
- It also functions as a standalone encoder and achieves state-of-the-art performance on forecasting and classification tasks.
- The retriever offers dual utility as an encoder for downstream applications and a general-purpose tool to enhance time-series models.
- TRACE employs hard negative mining for semantically meaningful retrieval.
- It enables fine-grained channel-level alignment and effectively handles multi-channel signals.
- The retriever can be task-specifically tuned for context-aware representations.
- TRACE's performance has been validated through extensive experiments across various domains.
- The proposal aims to improve time-series retrieval and enhance interpretability in downstream tasks.
- The method supports both Text-to-Timeseries and Timeseries-to-Text retrieval modes.
- TRACE bridges the gap in effective interpretation and retrieval of domain-specific time-series data.
Read Full Article
5 Likes
For uninterrupted reading, download the app